


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase					:       List[str]			    =     argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
UpperCamelCase					:       Any			    =     parser.parse_args()


UpperCamelCase					:       Tuple			    =     """cpu"""
UpperCamelCase					:       int			    =     """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

UpperCamelCase					:       List[Any]			    =     """path-to-your-trained-model"""
UpperCamelCase					:       Dict			    =     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
    UpperCamelCase					:       Tuple			    =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCamelCase					:       Dict			    =     pipe.to(device)

# to channels last
UpperCamelCase					:       Optional[int]			    =     pipe.unet.to(memory_format=torch.channels_last)
UpperCamelCase					:       Union[str, Any]			    =     pipe.vae.to(memory_format=torch.channels_last)
UpperCamelCase					:       str			    =     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
    UpperCamelCase					:       str			    =     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
UpperCamelCase					:       str			    =     torch.randn(2, 4, 64, 64)
UpperCamelCase					:       Union[str, Any]			    =     torch.rand(1) * 999
UpperCamelCase					:       Any			    =     torch.randn(2, 77, 768)
UpperCamelCase					:       Tuple			    =     (sample, timestep, encoder_hidden_status)
try:
    UpperCamelCase					:       Union[str, Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    UpperCamelCase					:       Optional[Any]			    =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       str			    =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase					:       Tuple			    =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    UpperCamelCase					:       Any			    =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCamelCase					:       Optional[int]			    =     666
UpperCamelCase					:       Any			    =     torch.Generator(device).manual_seed(seed)
UpperCamelCase					:       int			    =     {"""generator""": generator}
if args.steps is not None:
    UpperCamelCase					:       Tuple			    =     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
    UpperCamelCase					:       str			    =     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()